Summaries of papers

Allen, W. (accepted, 2017) ‘Making corpus data visible: lessons for visualising corpora and corpus analysis’, Corpora.

Researchers using corpora can visualise their data and analyses using a growing number of tools. In an environment where researchers are increasingly expected to work with non-academic partners under the auspices of ‘knowledge exchange’ or ‘impact’, and corpus data are more available thanks to digital methods, visualisation is especially valuable. But, although the field of corpus linguistics continues to generate its own range of techniques, it largely remains oriented towards finding ways for linguists to communicate results with other linguists rather than with or through non-experts. It also features a lack of discussion about how communication, motivations, and values also feature in the decision-making process of visualising corpora. These factors are arguably just as influential for the shape of the final product as technical aspects. This paper aims to open up this process by reporting on two corpus-based projects conducted by a non-expert research intermediary. Comparing the visualisation outputs of these projects, as well as the rationales for key decisions throughout their creation, leads to broader non-technical lessons for anyone wanting to visualise corpora: consider the aims and values of partners; develop communication strategies that acknowledge different areas of expertise; and link visualisation choices with wider project objectives.

Hill, R., Kennedy, H. and Gerarrd, Y. (2016) ‘Visualising junk: big data visualisations and the need for feminist data studies’, Journal of Communication Inquiry.

The datafication of culture has led to an increase in the circulation of data visualizations. In their production, visualizers draw on historical antecedents to define what constitutes a good visualization. In their reception, audiences similarly draw on experiences with visualizations and other visual forms to categorize them as good or bad. Whilst there are often sound reasons for such assessments, the gendered dimensions of judgements of cultural artefacts like data visualizations cannot be ignored. In this paper, we highlight how definitions of visualizations as bad can often be gendered. In turn, this gendered derision is often entangled with more legitimate criticisms of poor visualization execution, making it hard to see and so normalised. This, we argue, is a form of what Gill (2011) calls flexible sexism, and it is why there is a need not just for feminist critiques of big data, but for feminist data studies – that is, feminists doing big data and data visualization.

Kennedy, H. and Allen, W. (forthcoming) ‘Data visualisation as an emerging tool for online research’, in N.G. Fielding, R.M. Lee and G. Blank (eds) The Sage Handbook of Online Research Methods, 2nd edition, London: Sage.

This chapter provides an overview of data visualisation as an increasingly important method in the online research toolset and a way of communicating research results to the wider public. It starts by defining what a data visualisation is, and then reviews claims about what data visualisations can or cannot do. Considering the limits and possibilities of data visualisation in different contexts is an important component of good, reflective visualisation practice, and an essential precursor to engaging in such practice. With this framework established, the chapter discusses dominant visualisation tools and techniques and the range of considerations involved in visualisation design, noting the challenge to online researchers in developing the skills to engage in this process. The chapter then discusses examples of data visualisation within social science research, some of which are drawn from Allen’s own practice at The Migration Observatory at the University of Oxford. The chapter concludes by noting the importance of understanding audiences and the different contexts in which data visualisation might be used.

Kennedy, H. and Hill, R (2016) ‘The pleasure and pain of visualising data in times of data power’, Television and New Media.

This paper reflects on the growing compulsion amongst researchers to visualise large-scale digital data. It argues that the urge to visualise needs to be understood as a complex entanglement of a) the pragmatics of data visualisation, b) the problematic ideological work that visualisations do, c) the politics of data power and neoliberalism, and d) visualisation pleasures. The paper begins by outlining the considerations that constitute data visualisation design, highlighting the complexity of the process. It then provides an overview of critical debates about the way that visualisations work which are relevant to reflective visualisation practice. Then it turns to the context (of datafication and the neoliberalisation of the university) in which academic researchers contemplate visualisation futures and which simultaneously constrains the realisation of these futures. Finally, the paper acknowledges the cracks in these structures, the pleasure of visualising data, for example in using visualisation for advocacy and social justice. It concludes by reflecting on the implications of this entanglement.

Kennedy, H. and Hill, R. (2016) ‘Seeing Data, Feeling Numbers: emotions in everyday engagements with data and their visualisation’, Sociology.

This paper highlights the important role that emotions play in everyday engagements with data and their visualisation. To date, the relationship between data and emotions has rarely been noted or discussed – in part because data studies, on the whole, have not attended to ordinary, non-expert citizens’ relationships with data. In the paper, we draw on an empirical study of how people relate to data through visualisations to show a wide range of emotional engagements with diverse aspects of data and their visualisation, and so demonstrate the importance of emotions as vital components of making sense of data. In so doing, we nuance the argument that times of datafication, in which numbers, metrics and statistics dominate, are characterised by a renewed faith in objectivity and rationality. We argue that in datafied times, it is not only numbers but also the feeling of numbers, or how numbers feel, that is important.

Kennedy, H., Hill, R., Aiello, G. and Allen, W. (2016) ‘The work that visualisation conventions do’, Information, Communication and Society.

This paper argues that visualisation conventions work to make the data represented within visualisations seem objective, that is, transparent and factual. Interrogating the work that visualisation conventions do helps us to make sense of the apparent contradiction between criticisms of visualisations as doing persuasive work and visualisation designers’ belief that through visualisation, it is possible to ‘do good with data’ (Periscopic, 2014). We focus on four conventions which imbue visualisations with a sense of objectivity, transparency and facticity. These include: a) two-dimensional viewpoints; b) clean layouts; c) geometric shapes and lines; d) the inclusion of data sources. We argue that thinking about visualisations from a social semiotic standpoint, as we do in this paper by bringing together what visualisation designers say about their intentions with a semiotic analysis of the visualisations they produce, advances understanding of the ways that data visualisations come into being, how they are imbued with particular qualities and how power operates in and through them. Thus this paper contributes nuanced understanding of data visualisations and their production, by uncovering the ways in which power is at work within them. In turn, it advances debate about data in society and the emerging field of data studies.

Kennedy, H., Hill, R., Allen, W. and Kirk, A. (2016) ‘Engaging with (big) data visualisations: factors that effect engagement and resulting new definitions of effectiveness’, First Monday.

As data become increasingly ubiquitous, so too do data visualisations, which are the main means through which non-experts get access to data. Most visualizations circulate and are shared online, and many of them are produced by Internet researchers. For these reasons, data visualization is an important object of study for Internet research. This paper proposes that Internet research should engage critically with data visualization, and it does so by focusing on how people engage with them. Drawing on qualitative, empirical research with users, in this paper we identify six factors that affect engagement, which we define as socio-cultural: subject matter; source/media location; beliefs and opinions; time; emotions; and confidence and skills. We argue that our findings have implications for how effectiveness is defined in relation to data visualizations: such definitions vary depending on how, by whom, where and for what purpose visualizations are encountered. Our research also suggests that research into visualization engagement can benefit from adopting qualitative approaches developed within media audience research.